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<h2><a class="header" href="#comparing-performance-loops-vs-iterators" id="comparing-performance-loops-vs-iterators">Comparing Performance: Loops vs. Iterators</a></h2>
<p>To determine whether to use loops or iterators, you need to know which version
of our <code>search</code> functions is faster: the version with an explicit <code>for</code> loop or
the version with iterators.</p>
<p>We ran a benchmark by loading the entire contents of <em>The Adventures of
Sherlock Holmes</em> by Sir Arthur Conan Doyle into a <code>String</code> and looking for the
word <em>the</em> in the contents. Here are the results of the benchmark on the
version of <code>search</code> using the <code>for</code> loop and the version using iterators:</p>
<pre><code class="language-text">test bench_search_for ... bench: 19,620,300 ns/iter (+/- 915,700)
test bench_search_iter ... bench: 19,234,900 ns/iter (+/- 657,200)
</code></pre>
<p>The iterator version was slightly faster! We wont explain the benchmark code
here, because the point is not to prove that the two versions are equivalent
but to get a general sense of how these two implementations compare
performance-wise.</p>
<p>For a more comprehensive benchmark, you should check using various texts of
various sizes as the <code>contents</code>, different words and words of different lengths
as the <code>query</code>, and all kinds of other variations. The point is this:
iterators, although a high-level abstraction, get compiled down to roughly the
same code as if youd written the lower-level code yourself. Iterators are one
of Rusts <em>zero-cost abstractions</em>, by which we mean using the abstraction
imposes no additional runtime overhead. This is analogous to how Bjarne
Stroustrup, the original designer and implementor of C++, defines
<em>zero-overhead</em> in “Foundations of C++” (2012):</p>
<blockquote>
<p>In general, C++ implementations obey the zero-overhead principle: What you
dont use, you dont pay for. And further: What you do use, you couldnt hand
code any better.</p>
</blockquote>
<p>As another example, the following code is taken from an audio decoder. The
decoding algorithm uses the linear prediction mathematical operation to
estimate future values based on a linear function of the previous samples. This
code uses an iterator chain to do some math on three variables in scope: a
<code>buffer</code> slice of data, an array of 12 <code>coefficients</code>, and an amount by which
to shift data in <code>qlp_shift</code>. Weve declared the variables within this example
but not given them any values; although this code doesnt have much meaning
outside of its context, its still a concise, real-world example of how Rust
translates high-level ideas to low-level code.</p>
<pre><code class="language-rust ignore">let buffer: &amp;mut [i32];
let coefficients: [i64; 12];
let qlp_shift: i16;
for i in 12..buffer.len() {
let prediction = coefficients.iter()
.zip(&amp;buffer[i - 12..i])
.map(|(&amp;c, &amp;s)| c * s as i64)
.sum::&lt;i64&gt;() &gt;&gt; qlp_shift;
let delta = buffer[i];
buffer[i] = prediction as i32 + delta;
}
</code></pre>
<p>To calculate the value of <code>prediction</code>, this code iterates through each of the
12 values in <code>coefficients</code> and uses the <code>zip</code> method to pair the coefficient
values with the previous 12 values in <code>buffer</code>. Then, for each pair, we
multiply the values together, sum all the results, and shift the bits in the
sum <code>qlp_shift</code> bits to the right.</p>
<p>Calculations in applications like audio decoders often prioritize performance
most highly. Here, were creating an iterator, using two adaptors, and then
consuming the value. What assembly code would this Rust code compile to? Well,
as of this writing, it compiles down to the same assembly youd write by hand.
Theres no loop at all corresponding to the iteration over the values in
<code>coefficients</code>: Rust knows that there are 12 iterations, so it “unrolls” the
loop. <em>Unrolling</em> is an optimization that removes the overhead of the loop
controlling code and instead generates repetitive code for each iteration of
the loop.</p>
<p>All of the coefficients get stored in registers, which means accessing the
values is very fast. There are no bounds checks on the array access at runtime.
All these optimizations that Rust is able to apply make the resulting code
extremely efficient. Now that you know this, you can use iterators and closures
without fear! They make code seem like its higher level but dont impose a
runtime performance penalty for doing so.</p>
<h2><a class="header" href="#summary" id="summary">Summary</a></h2>
<p>Closures and iterators are Rust features inspired by functional programming
language ideas. They contribute to Rusts capability to clearly express
high-level ideas at low-level performance. The implementations of closures and
iterators are such that runtime performance is not affected. This is part of
Rusts goal to strive to provide zero-cost abstractions.</p>
<p>Now that weve improved the expressiveness of our I/O project, lets look at
some more features of <code>cargo</code> that will help us share the project with the
world.</p>
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